A National Review of Amplitude Integrated Electroencephalography (Aeeg) A

Total Page:16

File Type:pdf, Size:1020Kb

A National Review of Amplitude Integrated Electroencephalography (Aeeg) A Issue: Ir Med J; Vol 113; No. 9; P192 A National Review of Amplitude Integrated Electroencephalography (aEEG) A. Jenkinson1, M.A. Boyle2, D. Sweetman1 1. The National Maternity Hospital, Dublin. 2. The Rotunda Hospital, Dublin. Dear Sir, The use of amplitude integrated encephalography (aEEG) has become well integrated into routine care in the neonatal intensive care unit (NICU), to monitor brain function where encephalopathy or seizures are suspected.1 Early aEEG abnormalities and their rate of resolution have clinical and prognostic significance in severely ill newborns with altered levels of consciousness. 2 The most common use of aEEG is in infants with neonatal encephalopathy undergoing therapeutic hypothermia (TH). While TH is performed in tertiary centres, local and regional centres play an important role in the diagnosis and initial management of these infants. The Therapeutic Hypothermia Working Group National Report from 2018 reported that 28/69 (40%) of infants requiring Therapeutic Hypothermia were outborn in local or regional units requiring transfer.3 The report also highlighted that early and accurate aEEG interpretation is important in the care of these infants with many seizures being sub-clinical or electrographic without clinical correlation. We performed a national audit on aEEG in neonatal services in Ireland, surveying 18 neonatal units that are divided into Level 1 (Local), Level 2 (Regional) and Level 3 (Tertiary). One unit was excluded from our study as it provides a continuous EEG monitoring service on a 24-hour basis as part of the Neonatal Brain Research Group. Our findings showed that while no Level 1 unit had aEEG, it is widely available in Level 2 and Level 3 units [3/4 (75%) and 3/3 (100%)] respectively. With regards to electrode availability, 1/6 (16%) of centres had hydrogel electrodes. This represents a substandard level of care, given that hydrogel electrodes are the standard of care for preterm infants. The responsibility of electrode placement and aEEG set-up lay mostly with nursing staff (3/5 [60%]), with some inter-hospital variability. Interpretation was largely the responsibility of the doctor (4/5 [80%]). With regards to education, bedside teaching was the baseline standard of education provided by all units, with one unit relying on teaching from the manufacturing company supplying its equipment. Additional education was provided by two level 3 facilities, in the form of didactic lectures and small group tutorials. Of note, one Level 2 unit reported that while they possessed the equipment, the technology was not generally utilised in SCBU for encephalopathic infants due to a lack of staff education. This study group has previously shown that trainee paediatric doctors in Ireland have poor baseline knowledge of aEEG set-up, use and interpretation, with improvement noted following a structured education programme. Subsequent to this, and in conjunction with the Royal College of Physicians in Ireland (RCPI) and the Therapeutic Hypothermia Working Group, an online module titled ‘aEEG in the NICU’ will be launched in 2020. This will allow for a standardisation of aEEG education for neonatal staff nationally. Accurate use of aEEG is important in the diagnosis and management of neurologically compromised infants. Having highlighted its widespread use in units throughout Ireland, we hope the development of a standardised online education programme will help to educate and empower the neonatal workforce to provide the optimal standard of neurocritical care to newborns nationally. Corresponding Author: Dr. Allan Jenkinson Department of Neonatology, The National Maternity Hospital, Dublin, Ireland. Email: [email protected] References: 1. de Vries LS, Hellström-Westas L. Role of cerebral function monitoring in the newborn. Archives of Disease in Childhood - Fetal and Neonatal Edition. 2005;90(3):F201-FF7. 2. Murdoch-Eaton D DM, Livingston J. Measurement of electrical brain activity is recognized to have prognostic significance in severely ill children with altered consciousness. Dev Med Child Neurol 2001;43(2):91-6. 3. Meaney S MJ, Corcoran P, McKenna P, Filan P, Greene RA, Murphy J on behalf of Neonatal Therapeutic Hypothermia Working Group. Neonatal Therapeutic Hypothermia in Ireland, Annual Report 2018. Cork: National Perinatal Epidemiology Centre, 2020. .
Recommended publications
  • Electroencephalography (EEG)-Based Brain Computer Interfaces for Rehabilitation
    Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2012 Electroencephalography (EEG)-based brain computer interfaces for rehabilitation Dandan Huang Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.vcu.edu/etd Part of the Biomedical Engineering and Bioengineering Commons © The Author Downloaded from https://scholarscompass.vcu.edu/etd/2761 This Dissertation is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. School of Engineering Virginia Commonwealth University This is to certify that the dissertation prepared by Dandan Huang entitled “Electroencephalography (EEG)-based brain computer interfaces for rehabilitation” has been approved by her committee as satisfactory completion of the dissertation requirement for the degree of Doctoral of Philosophy. Ou Bai, Ph.D., Director of Dissertation, School of Engineering Ding-Yu Fei, Ph.D., School of Engineering Azhar Rafiq, M.D., School of Medicine Martin L. Lenhardt, Ph.D., School of Engineering Kayvan Najarian, Ph.D., School of Engineering Gerald Miller, Ph.D., Chair, Department of Biomedical Engineering, School of Engineering Rosalyn Hobson, Ph.D., Associate Dean of Graduate Studies, School of Engineering Charles J. Jennett, Ph.D., Dean, School of Engineering F. Douglas Boudinot, Ph.D., Dean of the Graduate School ______________________________________________________________________ Date © Dandan Huang 2012 All Rights Reserved ELECTROENCEPHALOGRAPHY-BASED BRAIN-COMPUTER INTERFACES FOR REHABILITATION A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University.
    [Show full text]
  • Using Amplitude-Integrated EEG in Neonatal Intensive Care
    Journal of Perinatology (2010) 30, S73–S81 r 2010 Nature America, Inc. All rights reserved. 0743-8346/10 www.nature.com/jp REVIEW Using amplitude-integrated EEG in neonatal intensive care JD Tao and AM Mathur Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA monitoring can be applied, for example, to infants admitted with The implementation of amplitude-integrated electroencephalography encephalopathy, seizures or other brain malformations. Both term (aEEG) has enhanced the neurological monitoring of critically ill infants. and preterm infants may benefit from the clinical application of Limited channel leads are applied to the patient and data are displayed in a aEEG findings. Much similar to continuous monitoring of vital semilogarithmic, time-compressed scale. Several classifications are currently signs, bedside aEEG can help guide decision-making in real time in use to describe patient tracings, incorporating voltage criteria, pattern for infants admitted to the NICU. This article will review the recognition, cyclicity, and the presence or absence of seizures. In term history, classification and application of aEEG in both term and neonates, aEEG has been used to determine the prognosis and treatment for preterm infants. those affected by hypoxic–ischemic encephalopathy, seizures, meningitis and even congenital heart disease. Its application as inclusion criteria for therapeutic hypothermia remains controversial. In preterm infants, Background normative values and patterns corresponding to gestational age are being 2 established. As these standards emerge, the predictive value of aEEG Starting in the 1960s, Maynard et al., developed the first use of increases, especially in the setting of preterm brain injury and an instrument to study cerebral activity in resuscitated patients with intraventricular hemorrhage.
    [Show full text]
  • Electroencephalography Source Connectivity: Toward High Time/Space Resolution Brain Networks
    Electroencephalography source connectivity: toward high time/space resolution brain networks Hassan M.1, 2 and Wendling F.1, 2 1 University of Rennes 1, LTSI, F-35000 Rennes, France 2INSERM, U1099, F-35000 Rennes, France Abstract— The human brain is a large-scale network which function depends on dynamic interactions between spatially-distributed regions. In the rapidly-evolving field of network neuroscience, two yet unresolved challenges are potential breakthroughs. First, functional brain networks should be estimated from noninvasive and easy to use neuroimaging techniques. Second, the time/space resolution of these techniques should be high enough to assess the dynamics of identified networks. Emerging evidence suggests that Electroencephalography (EEG) source connectivity method may offer solutions to both issues provided that scalp EEG signals are appropriately processed. Therefore, the performance of EEG source connectivity method strongly depends on signal processing (SP) that involves various methods such as preprocessing techniques, inverse solutions, statistical couplings between signals and network science. The main objective of this tutorial-like review is to provide an overview on EEG source connectivity. We describe the major contributions that the SP community brought to this research field. We emphasize the methodological issues that need to be carefully addressed to obtain relevant results and we stress the current limitations that need further investigation. We also report results obtained in concrete applications, in both normal and pathological brain states. Future directions in term of signal processing methods and applications are eventually provided. Index Terms— EEG signal processing, functional brain networks, EEG source connectivity, statistical couplings, inverse solutions. 1 I. INTRODUCTION Over the past decades, neuroscience research has significantly improved our understanding of the normal brain.
    [Show full text]
  • BIDS-EEG: an Extension to the Brain Imaging Data Structure (BIDS) Specification for Electroencephalography
    BIDS-EEG: an extension to the Brain Imaging Data Structure (BIDS) Specification for electroencephalography Cyril R Perneta,∗, Stefan Appelhoffb,∗, Guillaume Flandinc, Christophe Phillipsd, Arnaud Delormee,f, Robert Oostenveldg,h aThe University of Edinburgh, Centre for Clinical Brain Sciences (CCBS), Chancellor’s Building, 49 Little France Crescent, Edinburgh EH16 4SB bCenter for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany cWellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK dGIGA Institute, University of Liège, Liège, Belgium eSCCN, INC, UCSD, La Jolla, CA 95404, USA fCerco, CNRS/UPS, Toulouse, France gRadboud University, Donders Institute for Brain, Cognition and Behaviour. P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands hNatMEG, Karolinska Institutet, Stockholm, Sweden Abstract The Brain Imaging Data Structure (BIDS) project is a quickly evolving effort among the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. The first BIDS standard was proposed for the MRI/fMRI research community and has now been widely adopted. More recently a magnetoencephalography (MEG) data extension, BIDS-MEG, has been published. Here we present an extension to BIDS for electroencephalography (EEG) data, BIDS-EEG, along with tools and references to a series of public EEG datasets organized using this new standard. Keywords: Electroencephalography, EEG, Brain Imaging Data Structure (BIDS), BIDS-EEG, standardization, datasets, formatting, archive Introduction Electroencephalography (EEG) is now almost a century old, with its first application in humans in the 1920s (Berger, 1929). EEG records the electric potential fluctuations at the scalp, primarily from locally synchronous post-synaptic activity in the apical dendrites of pyramidal cells in the cortex.
    [Show full text]
  • Magnetoencephalography: Clinical and Research Practices
    brain sciences Review Magnetoencephalography: Clinical and Research Practices Jennifer R. Stapleton-Kotloski 1,2,*, Robert J. Kotloski 3,4 ID , Gautam Popli 1 and Dwayne W. Godwin 1,5 1 Department of Neurology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; [email protected] (G.P.); [email protected] (D.W.G.) 2 Research and Education, W. G. “Bill” Hefner Salisbury VAMC, Salisbury, NC 28144, USA 3 Department of Neurology, William S Middleton Veterans Memorial Hospital, Madison, WI 53705, USA; [email protected] 4 Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA 5 Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA * Correspondence: [email protected]; Tel.: +1-336-716-5243 Received: 28 June 2018; Accepted: 11 August 2018; Published: 17 August 2018 Abstract: Magnetoencephalography (MEG) is a neurophysiological technique that detects the magnetic fields associated with brain activity. Synthetic aperture magnetometry (SAM), a MEG magnetic source imaging technique, can be used to construct both detailed maps of global brain activity as well as virtual electrode signals, which provide information that is similar to invasive electrode recordings. This innovative approach has demonstrated utility in both clinical and research settings. For individuals with epilepsy, MEG provides valuable, nonredundant information. MEG accurately localizes the irritative zone associated with interictal spikes, often detecting epileptiform activity other methods cannot, and may give localizing information when other methods fail. These capabilities potentially greatly increase the population eligible for epilepsy surgery and improve planning for those undergoing surgery. MEG methods can be readily adapted to research settings, allowing noninvasive assessment of whole brain neurophysiological activity, with a theoretical spatial range down to submillimeter voxels, and in both humans and nonhuman primates.
    [Show full text]
  • Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: a Systematic Literature Review
    sensors Systematic Review Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review Nuraini Jamil 1 , Abdelkader Nasreddine Belkacem 2,* , Sofia Ouhbi 1 and Abderrahmane Lakas 2 1 Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] (N.J.); sofi[email protected] (S.O.) 2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] * Correspondence: [email protected] Abstract: Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive Citation: Jamil, N.; Belkacem, A.N.; BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used Ouhbi, S.; Lakas, A. Noninvasive for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from Electroencephalography Equipment the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited for Assistive, Adaptive, and to studies focusing on applications and devices of the BCI technology.
    [Show full text]
  • Dynamic Analysis of Human Brain in the Pain State by Electroencephalography
    Dynamic Analysis of Human Brain in the Pain State by Electroencephalography Mahsa Tavasoli Islamic Azad University Tehran North Branch zahra einalou ( [email protected] ) Islamic Azad University Tehran North Branch Reza Akhondzadeh Jondi Shapoor University of Medical Sciences: Ahvaz Jondishapour University of Medical Sciences Research note Keywords: Cold pressor test, EEG, Pain, Phasic pain, RQA Posted Date: March 2nd, 2021 DOI: https://doi.org/10.21203/rs.3.rs-275018/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Dynamic Analysis of Human Brain in the Pain State by Electroencephalography Mahsa Tavasoli1, Zahra Einalou1*, Reza Akhondzadeh2 *1 Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran Email: [email protected] 2 Department of Anesthesiology, Pain Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Email: [email protected] Corresponding Author: Zahra Einalou Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran Email: [email protected] Tel: 00989121754711 1 Abstract Objective: Pain is an unpleasant sensation that is important in all therapeutic conditions. So far, some studies have focused on pain assessment and cognition through different tests and methods. Considering the occurrence of pain causes, along with the activation of a long network in brain regions, recognizing the dynamical changes of the brain in pain states is helpful for pain detection using the electroencephalogram (EEG) signal. Therefore, the present study addressed the above-mentioned issue by applying EEG at the time of inducing phasic pain. Results: Phasic pain was produced using coldness and then dynamical features via EEG were analyzed by the Recurrence Quantification Analysis (RQA) method, and finally, the Rough neural network classifier was utilized for achieving accuracy regarding detecting and categorizing pain and non-pain states, which was 95.25 4%.
    [Show full text]
  • Q5: What Is the Added Advantage of Doing an Electroencephalography (EEG) in People with Convulsive Epilepsy in Non- Specialist S
    Role of EEG in management of convulsive epilepsy Q5: What is the added advantage of doing an electroencephalography (EEG) in people with convulsive epilepsy in non- specialist settings in low and middle income countries? Background The EEG is a technical instrument that can be used to support the diagnosis of seizures, classify epilepsy and to provide information on risk factors for seizure recurrence. Epilepsy is, by definition, a clinical condition characterized by two or more unprovoked seizures 24 hours apart (ILAE, 1993). However, this diagnostic and prognostic aid has several limitations which need to be emphasized in light of the available literature. An evidence-based approach is thus needed to put the EEG into a correct perspective. For this reason, the main indications of the EEG (i.e., use in support for the diagnosis of seizures and epilepsy, recognition of specific epilepsy syndromes to predict efficacy and tolerability of treatment, and prognostic value) must be assessed separately. In this light, the validity, reliability and overall utility of the EEG must be assessed with reference to different settings, especially LAMIC. Population/Intervention(s)/Comparison/Outcomes (PICO) Population: children and adults with epilepsy from population-based and clinic-based settings Interventions: EEG Comparison: not applicable Outcomes: appropriate diagnosis management (seizure recurrence, mortality, adverse effects) sensitivity, specificity and predictive values of EEG for the diagnosis of epilepsy risk of seizure recurrence after the
    [Show full text]
  • Seizure Incidence in the Acute Postneurosurgical Period Diagnosed Using Continuous Electroencephalography
    CLINICAL ARTICLE J Neurosurg 130:1203–1209, 2019 Seizure incidence in the acute postneurosurgical period diagnosed using continuous electroencephalography Brin Freund, MD, John C. Probasco, MD, and Eva K. Ritzl, MD Department of Neurology, Johns Hopkins Hospital, Baltimore, Maryland OBJECTIVE Delay in diagnosis and subsequent treatment of nonconvulsive seizures can lead to worsened outcomes. The gold standard in detecting nonconvulsive seizures is continuous video-electroencephalography (cEEG). Compared to routine, 30-minute EEG, the use of cEEG increases the likelihood of capturing intermittent nonconvulsive seizures. Studies of critically ill patients in intensive care units demonstrate a particularly high rate of nonconvulsive seizures. Some of these studies included postneurosurgical patients, but often subanalyses of specific populations were not done. In particular, few studies have specifically evaluated postneurosurgical patients by using cEEG in the acute postoperative setting. Therefore, the incidence and predictors of acute postneurosurgical seizures are unclear. METHODS In this study, the authors focused on patients who were admitted to the neurological critical care unit follow- ing neurosurgery and who underwent cEEG monitoring within 72 hours of surgery. RESULTS A total of 105 cEEG studies were performed in 102 patients. Twenty-nine patients demonstrated electro- graphic (subclinical) seizures, of whom 10 had clinical seizures clearly documented either before or during cEEG moni- toring. Twenty-two patients had subclinical seizures only detected on cEEG, 19 of whom did not have clinical seizure activity at any point during hospitalization. Those with seizures were more likely to have had a history of epilepsy (p = 0.006). The EEG studies of patients with seizures were more likely to show lateralized periodic discharges (p = 0.012) and lateralized rhythmic delta activity (p = 0.012).
    [Show full text]
  • Continuous Sensorimotor Rhythm Based Brain Computer Interface
    www.nature.com/scientificdata OPEN Continuous sensorimotor rhythm DATA DescriPTOR based brain computer interface learning in a large population James R. Stieger1,2, Stephen A. Engel2 & Bin He 1 ✉ Brain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheel- chairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 diferent motor-imagery-based BCI tasks. The current dataset presents one of the largest and most complex SMR-BCI datasets publicly available to date and should be useful for the development of improved algorithms for BCI control. Background & Summary Millions of individuals live with paralysis1. Te emerging feld of neural prosthetics seeks to provide relief to these individuals by forging new pathways of communication and control2–6. Invasive techniques directly record neural activity from the cortex and translate neural spiking into actionable commands such as moving a robotic arm or even individuals’ own muscles7–13. However, one major limitation of this approach is that roughly half of all implantations fail within the frst year14.
    [Show full text]
  • Biosignal Processing and Computational Methods to Enhance Sensory Motor Neuroprosthetics Mitsuhiro Hayashibe, David Guiraud, Jose L
    Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics Mitsuhiro Hayashibe, David Guiraud, Jose L. Pons, Dario Farina To cite this version: Mitsuhiro Hayashibe, David Guiraud, Jose L. Pons, Dario Farina. Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics. Frontiers in Neuroscience, Frontiers, 2015, 9 (#434), 10.3389/fnins.2015.00434. lirmm-01235841 HAL Id: lirmm-01235841 https://hal-lirmm.ccsd.cnrs.fr/lirmm-01235841 Submitted on 17 May 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. EDITORIAL published: 05 November 2015 doi: 10.3389/fnins.2015.00434 Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics Mitsuhiro Hayashibe 1*, David Guiraud 1, Jose L. Pons 2 and Dario Farina 3 1 French Institute for Research in Computer Science and Automation, University of Montpellier, Montpellier, France, 2 Spanish National Research Council, Madrid, Spain, 3 Department of Neurorehabilitation Engineering, University Medical
    [Show full text]
  • Electroencephalography
    13081Journal ofNeurology, Neurosurgery, and Psychiatry 1994;57:1308-1319 J Neurol Neurosurg Psychiatry: first published as 10.1136/jnnp.57.11.1308 on 1 November 1994. Downloaded from NEUROLOGICAL INVESTIGATIONS Electroencephalography C D Binnie, P F Prior Genesis ofthe electroencephalogram cific diagnostic significance. Thus slowing The electroencephalogram (EEG) is a record- may arise from causes as diverse as cerebral ing of cerebral electrical potentials by elec- oedema or hypoxia, or systemic disorders trodes on the scalp. Cerebral electrical activity such as hepatic insufficiency. The most reli- includes action potentials that are brief and able abnormal EEG sign is reduction of nor- produce circumscribed electrical fields, and mal activity, ranging from reduced amplitude slower, more widespread, postsynaptic poten- over a past cerebral infarct or a subdural tials. The magnitude of the signal recorded haematoma, to electrocerebral silence in brain from a neural generator depends on the solid death. Spiky waveforms (epileptiform activity) angle subtended at the electrode. Conse- occur in epilepsy and in some patients with quently, the activity of a single neuron can be cerebral disorder but without seizures. recorded by an adjacent microelectrode, but Rhythmic slow activities may occur bilaterally not at a distant scalp electrode. Synchronous over the frontal or posterior temporal regions activity in a horizontal laminar aggregate of in patients with dysfunction of diencephalic or neurons with parallel orientation may, how- brainstem structures. ever, constitute a generator of sufficient extent The EEG is profoundly influenced by alter- to be detectable on the scalp. Thus the EEG ations in vigilance and also changes with age, is a spatiotemporal average of synchronous most noticeably during childhood.
    [Show full text]